References

Guide: https://github.com/tommytracey/AIND-Capstone https://tommytracey.github.io/AIND-Capstone/machine_translation.html

Why TimeDistributedDenseLayer: https://datascience.stackexchange.com/questions/10836/the-difference-between-dense-and-timedistributeddense-of-keras

Keras Documentation: https://tensorflow.rstudio.com/reference/keras/

Stackoverflow: https://stackoverflow.com/questions/10961141/setting-up-a-3d-matrix-in-r-and-accessing-certain-elements

Dataset: http://www.manythings.org/anki/

Attempt to train words using 8-10 Words accuracy could be due to PADDING

Importing of Libraries

library(deepviz)
language <- "French"
language_code <- "fr"
file_name <- paste0("translation_", language_code, ".csv")
train <- read.csv(file_name, encoding="UTF-8", stringsAsFactors=FALSE)
# language <- "Indonesian"
# language_code <- "ind"
# file_name <- paste0("translation_", language_code, ".csv")
# train <- read.csv(file_name, encoding="UTF-8", stringsAsFactors=FALSE)

Amending column names

colnames(train) <- c("English", language)
train

Tokenizer

tokenize <- function(x){
  tokenizer <- text_tokenizer(num_words = 1000000)
  fit_text_tokenizer(tokenizer, x)
  sequences <- texts_to_sequences(tokenizer, x)
  return(c(sequences, tokenizer))
}

Padding

pad <- function(x, length=NULL){
  return(pad_sequences(x, maxlen = length, padding = 'post'))
}

Subsetting to 8-10 words within the English sentence

Finding number of words within an sentence

# sentences_length_vec <- function(word_list){
#   output <- tokenize(word_list)
#   sentence_length <- c()
#   for(i in 1:length(word_list)){
#     sentence_length[i] <- length(output[[i]])
#   }
#   
#   sentence_length
# }
# 
# english_sentence_length <- sentences_length_vec(list(train[, 1])[[1]])
# other_sentence_length <- sentences_length_vec(list(train[, 2])[[1]])
# 
# 
# ## Adding each sentence length to the dataframe `train`
# train$english_length <- english_sentence_length 
# train$other_length <- other_sentence_length
# 
# tail(train)

Conducting the subset of the dataframe

# lower_bound_words <- 8; upper_bound_words <- 10 
# subset_train <- subset(train, 
#                        train$english_length >= lower_bound_words & train$english_length <= upper_bound_words 
#                        & train$other_length >= lower_bound_words & train$other_length <= upper_bound_words
#                          )
# 
# ## Checking for the number of rows within the new subsetted dataframe for testing purposes.
# head(subset_train)
# tail(subset_train)
# nrow(subset_train)

Example for Tokenisation & Padding

text_sentences = c('The quick brown fox jumps over the lazy dog .',
    'By Jove , my quick study of lexicography won a prize .',
    'This is a short sentence .')
token_index <- length(text_sentences) + 1
output <- tokenize(text_sentences)
Loaded Tensorflow version 2.8.0
text_tokenized <- output[1:length(text_sentences)]
# print(output)

# Finding out the integer allocation to each word
tk <- output[[token_index]]$word_index
# print(tk)
# print(length(tk))
# print(table(tk))

Seeing the input vs output for each tokenized sentences

for(i in 1:length(text_sentences)){
  print(paste0("Sequence in Text ", i, ":"))
  print(paste0("Input: ", text_sentences[i]))
  print(paste0("Output: ", list(text_tokenized[[i]])))
  cat("\n")
}
[1] "Sequence in Text 1:"
[1] "Input: The quick brown fox jumps over the lazy dog ."
[1] "Output: c(1, 2, 4, 5, 6, 7, 1, 8, 9)"

[1] "Sequence in Text 2:"
[1] "Input: By Jove , my quick study of lexicography won a prize ."
[1] "Output: c(10, 11, 12, 2, 13, 14, 15, 16, 3, 17)"

[1] "Sequence in Text 3:"
[1] "Input: This is a short sentence ."
[1] "Output: c(18, 19, 3, 20, 21)"

Padding each tokenized sentences

# padded_text <- pad(text_tokenized)
# for(i in 1:length(text_sentences)){
#   print(paste0("Sequence in Text ", i, ":"))
#   print(paste0("Input: ", text_sentences[i]))
#   print(paste0("Output: ", list(text_tokenized[[i]])))
#   print(paste0("Output (Padded): ", list(padded_text[i,])))
# }

Preprocessing Component (Tidying up of characters and sentences)

Getting Compiled English Text (Testing)

for(i in 1:n){
  # if(i %% 100 != 0) next
  print(paste0("Sequence in Text ", i, ":"))
  print(paste0("Input: ", word_list[i]))
  print(paste0("Output: ", list(new_text_tokenized[[i]])))
  print(paste0("Output (Padded): ", list(new_padded_text[i,])))
  cat("\n")
}
[1] "Sequence in Text 1:"
[1] "Input: new jersey est parfois calme pendant l' automne , et il est neigeux en avril ."
[1] "Output: c(15, 16, 1, 6, 7, 17, 18, 19, 3, 4, 1, 20, 2, 21)"
[1] "Output (Padded): c(15, 16, 1, 6, 7, 17, 18, 19, 3, 4, 1, 20, 2, 21)"

[1] "Sequence in Text 2:"
[1] "Input: les états-unis est généralement froid en juillet , et il gèle habituellement en novembre ."
[1] "Output: c(8, 9, 10, 1, 5, 11, 2, 22, 3, 4, 23, 24, 2, 25)"
[1] "Output (Padded): c(8, 9, 10, 1, 5, 11, 2, 22, 3, 4, 23, 24, 2, 25)"

[1] "Sequence in Text 3:"
[1] "Input: california est généralement calme en mars , et il est généralement chaud en juin ."
[1] "Output: c(26, 1, 5, 7, 2, 27, 3, 4, 1, 5, 28, 2, 12)"
[1] "Output (Padded): c(26, 1, 5, 7, 2, 27, 3, 4, 1, 5, 28, 2, 12, 0)"

[1] "Sequence in Text 4:"
[1] "Input: les états-unis est parfois légère en juin , et il fait froid en septembre ."
[1] "Output: c(8, 9, 10, 1, 6, 29, 2, 12, 3, 4, 30, 11, 2, 31)"
[1] "Output (Padded): c(8, 9, 10, 1, 6, 29, 2, 12, 3, 4, 30, 11, 2, 31)"

[1] "Sequence in Text 5:"
[1] "Input: votre moins aimé fruit est le raisin , mais mon moins aimé est la pomme ."
[1] "Output: c(32, 13, 14, 33, 1, 34, 35, 36, 37, 13, 14, 1, 38, 39)"
[1] "Output (Padded): c(32, 13, 14, 33, 1, 34, 35, 36, 37, 13, 14, 1, 38, 39)"

[1] "Sequence in Text 6:"
[1] "Input: NA"
Error in new_text_tokenized[[i]] : subscript out of bounds

Getting Compiled Other Language Text (Testing)

# n <- nrow(subset_train)
n <- 5
word_list <- list(train[, 2])[[1]][1:n]
new_output <- tokenize(word_list)
new_text_tokenized <- new_output[1:n]
new_padded_text <- pad(new_text_tokenized)

for(i in 1:n){
  # if(i %% 100 != 0) next
  print(paste0("Sequence in Text ", i, ":"))
  print(paste0("Input: ", word_list[i]))
  print(paste0("Output: ", list(new_text_tokenized[[i]])))
  print(paste0("Output (Padded): ", list(new_padded_text[i,])))
}
[1] "Sequence in Text 1:"
[1] "Input: new jersey est parfois calme pendant l' automne , et il est neigeux en avril ."
[1] "Output: c(15, 16, 1, 6, 7, 17, 18, 19, 3, 4, 1, 20, 2, 21)"
[1] "Output (Padded): c(15, 16, 1, 6, 7, 17, 18, 19, 3, 4, 1, 20, 2, 21)"
[1] "Sequence in Text 2:"
[1] "Input: les états-unis est généralement froid en juillet , et il gèle habituellement en novembre ."
[1] "Output: c(8, 9, 10, 1, 5, 11, 2, 22, 3, 4, 23, 24, 2, 25)"
[1] "Output (Padded): c(8, 9, 10, 1, 5, 11, 2, 22, 3, 4, 23, 24, 2, 25)"
[1] "Sequence in Text 3:"
[1] "Input: california est généralement calme en mars , et il est généralement chaud en juin ."
[1] "Output: c(26, 1, 5, 7, 2, 27, 3, 4, 1, 5, 28, 2, 12)"
[1] "Output (Padded): c(26, 1, 5, 7, 2, 27, 3, 4, 1, 5, 28, 2, 12, 0)"
[1] "Sequence in Text 4:"
[1] "Input: les états-unis est parfois légère en juin , et il fait froid en septembre ."
[1] "Output: c(8, 9, 10, 1, 6, 29, 2, 12, 3, 4, 30, 11, 2, 31)"
[1] "Output (Padded): c(8, 9, 10, 1, 6, 29, 2, 12, 3, 4, 30, 11, 2, 31)"
[1] "Sequence in Text 5:"
[1] "Input: votre moins aimé fruit est le raisin , mais mon moins aimé est la pomme ."
[1] "Output: c(32, 13, 14, 33, 1, 34, 35, 36, 37, 13, 14, 1, 38, 39)"
[1] "Output (Padded): c(32, 13, 14, 33, 1, 34, 35, 36, 37, 13, 14, 1, 38, 39)"

Preprocessing both languages compilations

preprocess_text <- function(x, y){
  output_x <- tokenize(x)
  output_y <- tokenize(y)
  
  preprocess_x <- output_x[1:length(x)]; x_tk <- output_x[[length(x) + 1]]$word_index
  preprocess_y <- output_y[1:length(y)]; y_tk <- output_y[[length(y) + 1]]$word_index
  
  # print(preprocess_x)
  
  preprocess_x <- pad(preprocess_x)
  preprocess_y <- pad(preprocess_y)
  
  # print(preprocess_x)
  
  # Converting from a 2D matrix to a 3D tensor
  # preprocess_x <- array(preprocess_x[[1]], c(dim(preprocess_x[[1]])[1], dim(preprocess_x[[1]])[2], 1))
  # preprocess_y <- array(preprocess_y[[1]], c(dim(preprocess_y[[1]])[1], dim(preprocess_y[[1]])[2], 1))
  
  return(list(preprocess_x, preprocess_y, x_tk, y_tk))
}

Full Data

train_x <- list(train[, 1])[[1]]
train_y <- list(train[, 2])[[1]]
# print(subset_train_x)

process_output <- preprocess_text(train_x, train_y)
# print(process_output[4],)
preprocess_x <- process_output[1]; preprocess_y <- process_output[2]; x_tk <- process_output[3]; y_tk <- process_output[4]
# print(preprocess_x[[1]])
# print(preprocess_y[[1]])


# Conversion back to list of words from tokenized word list
# attributes(x_tk[[1]])$names
# length(y_tk[[1]])

Subset data

# n <- nrow(train) #1000
# subset_train_x <- list(subset_train[, 1])[[1]][1:n]
# subset_train_y <- list(subset_train[, 2])[[1]][1:n]
# # print(subset_train_x)
# 
# process_output <- preprocess_text(subset_train_x, subset_train_y)
# # print(process_output[4],)
# preprocess_x <- process_output[1]; preprocess_y <- process_output[2]; x_tk <- process_output[3]; y_tk <- process_output[4]
# # print(preprocess_x[[1]])
# # print(preprocess_y[[1]])
# 
# 
# # Conversion back to list of words from tokenized word list
# # attributes(x_tk[[1]])$names
# # length(y_tk[[1]])

Obtaining the maximum column number and re-padding

col_x <- dim(preprocess_x[[1]])[2]
col_y <- dim(preprocess_y[[1]])[2]

if(col_x >= col_y){
  max_col <- col_x
}else{
  max_col <- col_y
}

tmp_x <- pad(preprocess_x[[1]], max_col)
tmp_y <- pad(preprocess_y[[1]], max_col)

Checking Correspondance between subset_train and tmp

row <- 5
head(tmp_x)
     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21]
[1,]   17   23    1    8   67    4   39    7    3     1    55     2    44     0     0     0     0     0     0     0     0
[2,]    5   20   21    1    9   62    4   43    7     3     1     9    51     2    45     0     0     0     0     0     0
[3,]   22    1    9   67    4   38    7    3    1     9    68     2    34     0     0     0     0     0     0     0     0
[4,]    5   20   21    1    8   64    4   34    7     3     1    57     2    42     0     0     0     0     0     0     0
[5,]   29   12   16   13    1    5   82    6   30    12    16     1     5    83     0     0     0     0     0     0     0
[6,]   31   11   13    1    5   84    6   30   11     1     5    82     0     0     0     0     0     0     0     0     0
train_x[row]
[1] "your least liked fruit is the grape , but my least liked is the apple ."

Calculating Sparsity

calculate_sparsity <- function(df_matrix){
  zero_count <- 0
  total_count <- nrow(df_matrix) * ncol(tmp_x)
  for(i in 1:nrow(df_matrix)){
    for(j in 1:ncol(df_matrix)){
      if(df_matrix[i, j] == 0){
        zero_count = zero_count + 1
      }
    }
  }
  zero_count/total_count
}

print(paste("The Sparsity of the matrix is: ", round(calculate_sparsity(tmp_x)*100, 2), "%"))
[1] "The Sparsity of the matrix is:  46.37 %"

Conversion of 2D matrix to tensor

convert2tensor <- function(preprocess_data){
  preprocess_data <- array(preprocess_data, c(dim(preprocess_data)[1], dim(preprocess_data)[2], 1))
  return(preprocess_data)
}

# array(preprocess_x[[1]], c(dim(preprocess_x[[1]])[1], dim(preprocess_x[[1]])[2], 1))
# dim(array(preprocess_x[[1]], c(dim(preprocess_x[[1]])[1], dim(preprocess_x[[1]])[2], 1)))[2:3]

Converting to tensor

tensor_x <- convert2tensor(tmp_x)
dim(tensor_x)
[1] 137860     21      1
tensor_x[1, , ]
 [1] 17 23  1  8 67  4 39  7  3  1 55  2 44  0  0  0  0  0  0  0  0
tensor_y <- convert2tensor(tmp_y)
# tensor_y

Converting the logits back to text


logits_to_text <- function(logits, tokenizer, predict=FALSE){
  tokenizer_words <- attributes(tokenizer[[1]])$names
  text <- c()
  if(predict == TRUE){
    logits <- logits - 1 ## For prediction conversion only 
  }
  for(i in logits){
    if(i == 0){
      text <- c(text, "<PAD>")
    }else{
      text <- c(text, tokenizer_words[i])
    }
  }
  return(text)
}

# Testing to convert the first row back to text
# preprocess_x[[1]][1, ]
# preprocess_x[[1]]
logits_to_text(preprocess_x[[1]][1, ], x_tk)
 [1] "new"       "jersey"    "is"        "sometimes" "quiet"     "during"    "autumn"    "and"       "it"        "is"        "snowy"    
[12] "in"        "april"     "<PAD>"     "<PAD>"    

Building a simple RNN model


history = model_RNN %>% fit(
  x = tensor_x, y = tensor_y,
  epochs           = 10,
  batch_size = 1024,
  validation_split = 0.2,
)
Epoch 1/10

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108/108 [==============================] - 62s 566ms/step - loss: 1.7713 - accuracy: 0.5672

108/108 [==============================] - 67s 615ms/step - loss: 1.7713 - accuracy: 0.5672 - val_loss: 1.2585 - val_accuracy: 0.6358
Epoch 2/10

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108/108 [==============================] - 65s 604ms/step - loss: 1.2232 - accuracy: 0.6423

108/108 [==============================] - 70s 649ms/step - loss: 1.2232 - accuracy: 0.6423 - val_loss: 1.1022 - val_accuracy: 0.6666
Epoch 3/10

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108/108 [==============================] - 66s 608ms/step - loss: 1.1041 - accuracy: 0.6608

108/108 [==============================] - 70s 653ms/step - loss: 1.1041 - accuracy: 0.6608 - val_loss: 1.0046 - val_accuracy: 0.6781
Epoch 4/10

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108/108 [==============================] - 66s 607ms/step - loss: 1.0366 - accuracy: 0.6710

108/108 [==============================] - 70s 653ms/step - loss: 1.0366 - accuracy: 0.6710 - val_loss: 0.9434 - val_accuracy: 0.6911
Epoch 5/10

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108/108 [==============================] - 67s 625ms/step - loss: 0.9995 - accuracy: 0.6770

108/108 [==============================] - 72s 670ms/step - loss: 0.9995 - accuracy: 0.6770 - val_loss: 0.9169 - val_accuracy: 0.6930
Epoch 6/10

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108/108 [==============================] - 66s 615ms/step - loss: 0.9589 - accuracy: 0.6855

108/108 [==============================] - 71s 660ms/step - loss: 0.9589 - accuracy: 0.6855 - val_loss: 0.8878 - val_accuracy: 0.7037
Epoch 7/10

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108/108 [==============================] - 67s 616ms/step - loss: 0.9394 - accuracy: 0.6884

108/108 [==============================] - 71s 662ms/step - loss: 0.9394 - accuracy: 0.6884 - val_loss: 0.8578 - val_accuracy: 0.7032
Epoch 8/10

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108/108 [==============================] - 67s 617ms/step - loss: 0.9072 - accuracy: 0.6945

108/108 [==============================] - 71s 662ms/step - loss: 0.9072 - accuracy: 0.6945 - val_loss: 0.8263 - val_accuracy: 0.7175
Epoch 9/10

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108/108 [==============================] - 72s 667ms/step - loss: 0.8940 - accuracy: 0.6965 - val_loss: 0.8250 - val_accuracy: 0.7019
Epoch 10/10

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108/108 [==============================] - 74s 684ms/step - loss: 0.8788 - accuracy: 0.6983 - val_loss: 0.8124 - val_accuracy: 0.7149
plot(history)
`geom_smooth()` using formula 'y ~ x'

Prediction

Obtaining the output for prediction (Testing)

predict_output <- model_RNN %>% predict(matrix(tensor_x[5, ,], nrow=1))
# predict_output


predict_output <- argmax(predict_output, FALSE)
# train_x[5]
train_y[5]
[1] "votre moins aimé fruit est le raisin , mais mon moins aimé est la pomme ."
logits_to_text(predict_output, y_tk, predict = TRUE)
 [1] "votre"  "fruit"  "est"    "moins"  "aimé"  "la"     "mais"   "mais"   "mon"    "moins"  "aimé"  "est"    "la"     "citron" "<PAD>" 
[16] "<PAD>"  "<PAD>"  "<PAD>"  "<PAD>"  "<PAD>"  "<PAD>" 

Predictions for Training set


pred_translation <- function(i){
  predict_output <- model_RNN %>% predict(matrix(tensor_x[i, ,], nrow=1))
  predict_output <- argmax(predict_output, FALSE)
  converted_text <- logits_to_text(predict_output, y_tk)
  converted_text[converted_text == "<PAD>"] <- ""
  converted_text <- trimws(paste(converted_text, collapse = " "))
  print(paste("Input sentence:", train_x[i]))
  print(paste("Intended Output Sentence:", train_y[i]))
  print(paste("Predicted Output Sentence:", converted_text))
}

## `i` represents the index within the training set.
pred_translation(5)
[1] "Input sentence: your least liked fruit is the grape , but my least liked is the apple ."
[1] "Intended Output Sentence: votre moins aimé fruit est le raisin , mais mon moins aimé est la pomme ."
[1] "Predicted Output Sentence: votre fruit est moins aimé la mais mais mon moins aimé est la citron"
---
title: "Language Translation with RNN"
author: "Max Lee & Yong Sheng"
date: "Term 7, 2022"
output: html_notebook
---

*References*

Guide:
https://github.com/tommytracey/AIND-Capstone
https://tommytracey.github.io/AIND-Capstone/machine_translation.html

Why TimeDistributedDenseLayer:
https://datascience.stackexchange.com/questions/10836/the-difference-between-dense-and-timedistributeddense-of-keras

Keras Documentation:
https://tensorflow.rstudio.com/reference/keras/

Stackoverflow:
https://stackoverflow.com/questions/10961141/setting-up-a-3d-matrix-in-r-and-accessing-certain-elements

Dataset:
http://www.manythings.org/anki/


*Attempt to train words using 8-10 Words* accuracy could be due to PADDING

# Importing of Libraries
```{r warning=FALSE,results='hide',error=FALSE,message=FALSE}
library(keras)
library(tensorflow)
library(tokenizers)
library(dplyr)
library(png)
library(reticulate)
library(abind)
library(ramify)
library(stringr)
library(deepviz)
```

```{r}
language <- "French"
language_code <- "fr"
file_name <- paste0("translation_", language_code, ".csv")
train <- read.csv(file_name, encoding="UTF-8", stringsAsFactors=FALSE)
```

```{r}
# language <- "Indonesian"
# language_code <- "ind"
# file_name <- paste0("translation_", language_code, ".csv")
# train <- read.csv(file_name, encoding="UTF-8", stringsAsFactors=FALSE)
```
## Amending column names
```{r}
colnames(train) <- c("English", language)
```

```{r}
train
```
# Tokenizer
```{r}
tokenize <- function(x){
  tokenizer <- text_tokenizer(num_words = 1000000)
  fit_text_tokenizer(tokenizer, x)
  sequences <- texts_to_sequences(tokenizer, x)
  return(c(sequences, tokenizer))
}
```

# Padding
```{r}
pad <- function(x, length=NULL){
  return(pad_sequences(x, maxlen = length, padding = 'post'))
}
```

# Subsetting to 8-10 words within the English sentence

## Finding number of words within an  sentence
```{r}
# sentences_length_vec <- function(word_list){
#   output <- tokenize(word_list)
#   sentence_length <- c()
#   for(i in 1:length(word_list)){
#     sentence_length[i] <- length(output[[i]])
#   }
#   
#   sentence_length
# }
# 
# english_sentence_length <- sentences_length_vec(list(train[, 1])[[1]])
# other_sentence_length <- sentences_length_vec(list(train[, 2])[[1]])
# 
# 
# ## Adding each sentence length to the dataframe `train`
# train$english_length <- english_sentence_length 
# train$other_length <- other_sentence_length
# 
# tail(train)

```

## Conducting the subset of the dataframe
```{r}
# lower_bound_words <- 8; upper_bound_words <- 10 
# subset_train <- subset(train, 
#                        train$english_length >= lower_bound_words & train$english_length <= upper_bound_words 
#                        & train$other_length >= lower_bound_words & train$other_length <= upper_bound_words
#                          )
# 
# ## Checking for the number of rows within the new subsetted dataframe for testing purposes.
# head(subset_train)
# tail(subset_train)
# nrow(subset_train)
```




# Example for Tokenisation & Padding
```{r}
text_sentences = c('The quick brown fox jumps over the lazy dog .',
    'By Jove , my quick study of lexicography won a prize .',
    'This is a short sentence .')
token_index <- length(text_sentences) + 1
output <- tokenize(text_sentences)
text_tokenized <- output[1:length(text_sentences)]
# print(output)

# Finding out the integer allocation to each word
tk <- output[[token_index]]$word_index
# print(tk)
# print(length(tk))
# print(table(tk))
```
## Seeing the input vs output for each tokenized sentences
```{r}
for(i in 1:length(text_sentences)){
  print(paste0("Sequence in Text ", i, ":"))
  print(paste0("Input: ", text_sentences[i]))
  print(paste0("Output: ", list(text_tokenized[[i]])))
  cat("\n")
}
```

## Padding each tokenized sentences
```{r}
# padded_text <- pad(text_tokenized)
# for(i in 1:length(text_sentences)){
#   print(paste0("Sequence in Text ", i, ":"))
#   print(paste0("Input: ", text_sentences[i]))
#   print(paste0("Output: ", list(text_tokenized[[i]])))
#   print(paste0("Output (Padded): ", list(padded_text[i,])))
# }
```



# Preprocessing Component (Tidying up of characters and sentences)

## Getting Compiled English Text (Testing)
```{r}
# n <- nrow(subset_train)
n <- 5
word_list <- list(train[, 1])[[1]][1:n]
# word_list
new_output <- tokenize(word_list)
new_text_tokenized <- new_output[1:n]
new_padded_text <- pad(new_text_tokenized)

for(i in 1:n){
  # if(i %% 100 != 0) next
  print(paste0("Sequence in Text ", i, ":"))
  print(paste0("Input: ", word_list[i]))
  print(paste0("Output: ", list(new_text_tokenized[[i]])))
  print(paste0("Output (Padded): ", list(new_padded_text[i,])))
  cat("\n")
}

```

## Getting Compiled Other Language Text (Testing)
```{r}
# n <- nrow(subset_train)
n <- 5
word_list <- list(train[, 2])[[1]][1:n]
new_output <- tokenize(word_list)
new_text_tokenized <- new_output[1:n]
new_padded_text <- pad(new_text_tokenized)

for(i in 1:n){
  # if(i %% 100 != 0) next
  print(paste0("Sequence in Text ", i, ":"))
  print(paste0("Input: ", word_list[i]))
  print(paste0("Output: ", list(new_text_tokenized[[i]])))
  print(paste0("Output (Padded): ", list(new_padded_text[i,])))
}

```


## Preprocessing both languages compilations
```{r}
preprocess_text <- function(x, y){
  output_x <- tokenize(x)
  output_y <- tokenize(y)
  
  preprocess_x <- output_x[1:length(x)]; x_tk <- output_x[[length(x) + 1]]$word_index
  preprocess_y <- output_y[1:length(y)]; y_tk <- output_y[[length(y) + 1]]$word_index
  
  # print(preprocess_x)
  
  preprocess_x <- pad(preprocess_x)
  preprocess_y <- pad(preprocess_y)
  
  # print(preprocess_x)
  
  # Converting from a 2D matrix to a 3D tensor
  # preprocess_x <- array(preprocess_x[[1]], c(dim(preprocess_x[[1]])[1], dim(preprocess_x[[1]])[2], 1))
  # preprocess_y <- array(preprocess_y[[1]], c(dim(preprocess_y[[1]])[1], dim(preprocess_y[[1]])[2], 1))
  
  return(list(preprocess_x, preprocess_y, x_tk, y_tk))
}
```

## Full Data
```{r}
train_x <- list(train[, 1])[[1]]
train_y <- list(train[, 2])[[1]]
# print(subset_train_x)

process_output <- preprocess_text(train_x, train_y)
# print(process_output[4],)
preprocess_x <- process_output[1]; preprocess_y <- process_output[2]; x_tk <- process_output[3]; y_tk <- process_output[4]
# print(preprocess_x[[1]])
# print(preprocess_y[[1]])


# Conversion back to list of words from tokenized word list
# attributes(x_tk[[1]])$names
# length(y_tk[[1]])
```


## Subset data
```{r}
# n <- nrow(train) #1000
# subset_train_x <- list(subset_train[, 1])[[1]][1:n]
# subset_train_y <- list(subset_train[, 2])[[1]][1:n]
# # print(subset_train_x)
# 
# process_output <- preprocess_text(subset_train_x, subset_train_y)
# # print(process_output[4],)
# preprocess_x <- process_output[1]; preprocess_y <- process_output[2]; x_tk <- process_output[3]; y_tk <- process_output[4]
# # print(preprocess_x[[1]])
# # print(preprocess_y[[1]])
# 
# 
# # Conversion back to list of words from tokenized word list
# # attributes(x_tk[[1]])$names
# # length(y_tk[[1]])
```

# Obtaining the maximum column number and re-padding
```{r}
col_x <- dim(preprocess_x[[1]])[2]
col_y <- dim(preprocess_y[[1]])[2]

if(col_x >= col_y){
  max_col <- col_x
}else{
  max_col <- col_y
}

tmp_x <- pad(preprocess_x[[1]], max_col)
tmp_y <- pad(preprocess_y[[1]], max_col)


```

# Checking Correspondance between subset_train and tmp
```{r}
row <- 5
head(tmp_x)
train_x[row]
```
## Calculating Sparsity
```{r}
calculate_sparsity <- function(df_matrix){
  zero_count <- 0
  total_count <- nrow(df_matrix) * ncol(tmp_x)
  for(i in 1:nrow(df_matrix)){
    for(j in 1:ncol(df_matrix)){
      if(df_matrix[i, j] == 0){
        zero_count = zero_count + 1
      }
    }
  }
  zero_count/total_count
}

print(paste("The Sparsity of the matrix is: ", round(calculate_sparsity(tmp_x)*100, 2), "%"))
```



# Conversion of 2D matrix to tensor
```{r}
convert2tensor <- function(preprocess_data){
  preprocess_data <- array(preprocess_data, c(dim(preprocess_data)[1], dim(preprocess_data)[2], 1))
  return(preprocess_data)
}

# array(preprocess_x[[1]], c(dim(preprocess_x[[1]])[1], dim(preprocess_x[[1]])[2], 1))
# dim(array(preprocess_x[[1]], c(dim(preprocess_x[[1]])[1], dim(preprocess_x[[1]])[2], 1)))[2:3]
```


# Converting to tensor
```{r}
tensor_x <- convert2tensor(tmp_x)
dim(tensor_x)
tensor_x[1, , ]
tensor_y <- convert2tensor(tmp_y)
# tensor_y
```



# Converting the logits back to text
```{r}

logits_to_text <- function(logits, tokenizer, predict=FALSE){
  tokenizer_words <- attributes(tokenizer[[1]])$names
  text <- c()
  if(predict == TRUE){
    logits <- logits - 1 ## For prediction conversion only 
  }
  for(i in logits){
    if(i == 0){
      text <- c(text, "<PAD>")
    }else{
      text <- c(text, tokenizer_words[i])
    }
  }
  return(text)
}

# Testing to convert the first row back to text
# preprocess_x[[1]][1, ]
# preprocess_x[[1]]
logits_to_text(preprocess_x[[1]][1, ], x_tk)

```


# Building a simple RNN model
```{r}
# dim(tensor_y)
model_RNN <-  keras_model_sequential()
model_RNN %>% 
  layer_simple_rnn(units = 256, input_shape = dim(tensor_x)[2:3], return_sequences = TRUE) %>%
  time_distributed(layer_dense(units = 1024, activation = 'relu'))%>%
  layer_dropout(rate = 0.5) %>%
  time_distributed(layer_dense(units = length(y_tk[[1]]) + 1, activation = 'softmax'))

model_RNN %>% summary()

model_RNN %>% compile(
  loss      = 'sparse_categorical_crossentropy',
  # optimizer = optimizer_rmsprop(),
  optimizer = optimizer_adam(learning_rate = 0.005),
  metrics=c('accuracy')
)

plot_model(model_RNN)
```
```{r}

history = model_RNN %>% fit(
  x = tensor_x, y = tensor_y,
  epochs           = 10,
  batch_size = 1024,
  validation_split = 0.2,
)
plot(history)
```


# Prediction


## Obtaining the output for prediction (Testing)
```{r}
predict_output <- model_RNN %>% predict(matrix(tensor_x[5, ,], nrow=1))
# predict_output


predict_output <- argmax(predict_output, FALSE)
# train_x[5]
train_y[5]
logits_to_text(predict_output, y_tk, predict = TRUE)

```
## Predictions for Training set
```{r}

pred_translation <- function(i){
  predict_output <- model_RNN %>% predict(matrix(tensor_x[i, ,], nrow=1))
  predict_output <- argmax(predict_output, FALSE)
  converted_text <- logits_to_text(predict_output, y_tk, predict = TRUE)
  converted_text[converted_text == "<PAD>"] <- ""
  converted_text <- trimws(paste(converted_text, collapse = " "))
  print(paste("Input sentence:", train_x[i]))
  print(paste("Intended Output Sentence:", train_y[i]))
  print(paste("Predicted Output Sentence:", converted_text))
}

## `i` represents the index within the training set.
pred_translation(5)

```

